The work in this year mainly involve three projects. First, for regression methods for bivariate failure time data, we have developed a statistical model and evaluated its performance through simulation studies. Also, the proposed methods have been applied to the WHI hormone therapy trials. Second, we proposed a improved family history score for risk prediction of breast cancer. This project was motivated by the Sister Study, where all participants have at least one sister with breast cancer. With traditional approach of considering family history as a yes/no variable, there is no ability to distinguish the risks of a women with 1 sister out of 10 with breast cancer versus a women with 3 sister out of 3 with breast cancer. The proposed family history score takes number of diseased sister, total number of sisters and ages of sisters into account. In our simulation and data analysis, the proposed family history score showed powerful predictiveness for breast cancer risk. Third, we investigated a statistical method to characterize the spatial and temporal distribution of breast cancer incidence, with application to SEER data and Sister Study data.